Phonemic Comparison of English and Punjabi
Bibliographic record
Abstract
English and Punjabi are languages which do not belong to the same families of languages. English is one of the West Germanic languages whereas; Punjabi is a part of the Indo-Aryan family. Punjabi is spoken by various nations on the globe, especially Pakistan and its province Punjab as well as in Indian Panjab. Both English and Punjabi manifest themselves through various dialects on the basis of diversified geographical areas. English is used as the first language by 379,007,140 speakers and further 753,359,540 speakers use it as a second language in more than 104 nations. So, the total speakers of English around the globe are 1,132,366,680 (Ethnologue, 2019). The importance of Punjabi cannot be denied being the 10th most widely used language on the globe (Ghai & Singh, 2013). According to Ethnologue (2019), the total number of Punjabi speakers is 125,326,840. In Pakistan, it is the language of the majority of the people residing in the most populous province of Pakistan, Punjab. It is among twenty-two languages that have obtained official status. Unfortunately, no considerable work has been done on its phonology. This study is an attempt to describe the phonemic differences between English and Punjabi by using the theoretical framework of the Levenshtein algorithm. The index of differences and similarities is determined through the inventories of both languages. The inventories are used as data in this research paper. The Levenshtein algorithm (Levenshtein, 1965) is used to analyse the inventories to calculate the ratio of differences and similarities. The outcome of the current research shows that both English and Punjabi have a phonemic similarity level of 56.25% whereas the index of difference is 43.75%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".